Negative Attitudes, Network and Education

Negative Attitudes, Network and Education

Patrick Bennett∗, Lisbeth la Cour∗, Birthe Larsen∗, Gisela Waisman†

March 2016

Abstract

This paper assesses, both theoretically and empirically, the potential explanations behind the educational gap between young natives and immigrants using two measures, negative atti- tudes towards immigrants and networking. The paper considers the impact of negative attitudes and networking and that these parameters may influence high and uneducated workers as well as immigrants and natives differently, creating different incentives to acquire education for the two ethnic groups. Theoretically, this paper concludes that if all immigrants are equally affected by discrimination, immigrants obtain less education than natives while if only low-educated im- migrants are affected by negative attitudes, immigrants obtain more education than natives to improve their employment prospects. Using rich Danish administrative data, this paper finds evidence consistent with this second case, that greater negative attitudes have a positive im- pact on male immigrants decision to acquire education and that networking can also increase immigrant education.

We want to thank participants at the Search and Matching conference in Edinburgh 2014, the Workshop on Gender and Ethnic Differences in Market Outcomes, Aix-en-Provence 2014, the Copenhagen Education Network Seminar December 2014, School of Economics, Singapore Management University 2015. the RES conference 2015 in Manchester, the EEA conference 2015 in Mannheim, the WEAI conference in Singapore 2016, and Bochum University 2015, Kevin Lang, John Kennes, Pietro Garibaldi, Linas Tarasonis, Dario Pozzoli, and Anna Piil Damm. Finally, we want to thank Simon Backlund for excellent research assistance.

∗_{Department of Economics, Copenhagen Business School.} †_{Regeringskansliet, Stockholm.}

1 Introduction

An OECD report from 2006 reveals that immigrant and immigrant offspring at a very young age express equal or sometimes even higher motivation to learn mathematics than their native counterparts and very positive attitudes towards school and education in general.1 _{However, at}

the age of 15, they under perform compared to the natives. More than a third of the first and second generation immigrant children in Austria, Belgium, Denmark, Germany, Norway and the USA, who have spent all their entire schooling in the host country, perform below the baseline PISA benchmark for mathematics performance, a period at which students begin to demonstrate the kind of skills that enable them to actively use mathematics.2 _{Furthermore, when taking their}

parental background into account, immigrants tend not to perform as well in school as their native peers.3 _{This fact may then, in turn, influence their choice of further education, and eventually}

their labour market outcome and performance.

When explaining the educational gap between immigrants and natives, measures which impact immigrants and natives differently are likely to be important. The aim of this paper is to discover the factors that shift the motivation and performance of immigrants when the decision about education beyond compulsory school is taken. For the educational decision, workers compare the value corresponding to acquiring education to the value of not acquiring education. These values depend on the expected incomes which are influenced by both the employment probability as well as wages. The novelty of this paper is to examine theoretically, as well as empirically, whether negative attitudes towards immigrants and networking could influence immigrant employment chances, as well as immigrant wages, differently for educated workers and uneducated workers compared to the same variables for natives. If this were the case, the value of acquiring education may be impacted differently for natives and immigrants and as such, may explain the educational gap between natives and immigrants.

In particular, we will examine the effect of negative attitudes towards immigrants in a region and potential impact of networking through individuals of the same ethnicity living in a region. Negative attitudes towards immigrants may cause discrimination, implying that workers are fired or decide to quit a job. This lowers the value of employment, through both shorter employment

1_{OECD 2006} 2_ibid.

periods and lower wages, as the bargaining power of immigrants falls which in turn affects the value of acquiring education.

There are some empirical papers on discrimination and employment and wages (see for example Waisman and Larsen 2015, Kofi Charles and Guryan 2008) but, to our knowledge, no papers on the additional impact through these channels on education. Concerning networking, immigrants from the same home country or region may increase the likelihood of getting a job and improve labour market performance. Hence, more well-educated immigrants from the same home country or region may increase the return of education, implying that more immigrants acquire education. This may work in different ways. Social networks may influence employment outcomes: the more employed contacts the individual has, the more likely it is that the individual will learn about new job openings (Calvo-Armengol and Jackson 2004, Hellerstein et al 2009) and networks may influence both wages and employment opportunities (Fontaine 2007, Galeanios 2014, Damm 2014). Similarly, empirical research confirms that (see for example Andersson et al 2009, Solignac and Tô 2015) more immigrants living in areas with a large number of employed neighbours are more likely to have jobs compared to immigrants living in areas with fewer employed neighbours. This could be due to networking and/or social norm effects. Furthermore, Kramarz and Skans (2014) show for Swedish data that family networks are important, in terms of obtaining the first job after graduation, and that this impact is stronger for youth of uneducated parents and immigrants in regions with high unemployment. Hence, networking may increase employment probability, and more networking among immigrants may, to some extent, offset the decrease in employment perspectives and wage modifications due to negative attitudes or discrimination.

We formulate a Becker-style taste discrimination model within a search and wage bargaining setting. Bowlus and Eckstein (2002), Flabbi (2010), Mailath et al. (2000), and Lang et al. (2005) study discrimination in the presence of search frictions but with no educational decision. We assume that potential negative tastes towards immigrants imply that their separation rate from the job is higher than the separation rate of a native worker. This may be due to both the worker deciding to quit and the employer firing the worker. This assumption allows us to assume that neither job searchers nor employers know whether discrimination will take place in a particular firm; all that is known is that immigrants face a higher separation rate than natives. We show that immigrants’ potential higher separation rate, ceteris paribus, also implies that their employment chances fall as firms, in turn, supply fewer vacancies. Natives and immigrants decide

whether to educate or not. They are aware of the existence of discrimination in the labour market and of the possibility of influencing their chances of getting employed through networking. In terms of negative attitudes towards immigrants, we consider two different cases. In the first case, all immigrant workers are affected by negative attitudes towards them and in the second, only low-educated workers are affected. The channel through which the educational level is affected by networking and negative attitudes in our model is through the impact on the expected employment perspectives. However, the possibility that negative attitudes also influence the value of being unemployed directly, that is, over and above the impact on wages and employment chances, could easily be included in the theoretical model and is consistent with the empirical analysis which we perform.

Empirically, we analyse the educational gap between immigrants and natives using Danish Register Data at both the municipality and individual level. Due to the excellent quality of the Danish Register Data, we have the whole population, can link to family members, and have information on employment, education, income, etc. More specifically, we analyse the impact of networking and negative attitudes on education by considering how young immigrants’ high school decision, which is not obligatory in Denmark, depends on the the number of people of their own nationality and negative attitudes towards immigrants in the area where they live relative to the impact on young natives. We examine this decision to attend high school as it is made at a young age, around 16, and individuals will usually be living at home while attending high school. The advantage of this is that the parents decide where to live and young immigrants and natives then decide whether to continue in high school. We therefore have that the household placement is, plausibly, exogenous for the person making the educational decision; that is the young immigrants and natives are not both deciding where to live and deciding whether to attend high school.

Despite this, there are concerns that unobservable factors could drive parents, either in their emigration or subsequent relocation, to locate in order to give the young immigrant or native a better choice of high school prior to this high school decision. While we control for parental characteristics together with a variety of municipality controls, we address these potential omitted unobservable characteristics in two ways. Firstly, we allow for the possibility that individuals can relocate due to educational considerations by estimating, as a robustness check, the high school decision for only those who have not recently moved. Secondly, we directly examine the importance of unobservable characteristics compared to observables in explaining our estimated effects using

a procedure developed in Oster (2015). While there are reasons to believe that concerns over the importance of unobservables may be mitigated due to the timing of the decision to attend high school, we are able to directly quantify how our estimates change depending on the degree of omitted variable bias due to unobservables.

In the macro-econometric level analysis, we exploit the panel nature of our data to control for unobserved time-invariant factors which affect the fraction of young individuals attending high school. We find positive, but imprecisely estimated, effects of negative attitudes on the fraction of immigrants attending high school. We see little impact of networking on high school attendance, and turn to analysis at the individual level to not only more precisely estimate an individual’s potential network but also take into account important family and individual level factors.

At the micro-econometric level we see a positive and significant impact of negative attitudes on male immigrants’ probability to attend high school and positive, but less precise effects for females. We see no effects, either positive or negative, for natives. We find that networking matters for immigrants, but indications that the quality of an immigrant’s potential network matters for males while only the quantity matters for females. These results on negative attitudes, and to a lesser extent networking, are robust to the exclusion of households who have moved recently, within the past 3 or 6 years. Under reasonable assumptions about the importance of unobservables, we are able to bound the estimated effect for males away from zero; that is we can state with a good deal of confidence that even when accounting for potentially correlated unobservables, negative attitudes have a positive impact on high school attendance for male immigrants. Lastly, we see that the negative attitudes measure matters only for 1st _{generation immigrants and not for 2}nd

generation immigrants, who are likely more assimilated and less likely to be adversely impacted by negative attitudes. Overall, our empirical findings are consistent with the second case of the theoretical model, where negative attitudes are prevalent only in the low-skilled sector and more severe negative attitudes increase the incentives of immigrants to acquire education.

The paper is structured as follows. In section 2 the model is setup, then the following sections consider the impact of negative attitudes towards immigrants and the fraction of immigrants. In Section 6 we consider heterogenous networking effects. Sections 7 and 8 provide a macro- econometric and a micro-econometric analysis. Section 9 explores the robustness of the micro- econometric results, and Section 10 concludes.

2 The Model

We consider a search and matching model with natives, N and immigrants, I, which may be educated with productivity yh _{or non-educated with productivity, y}l _{where y}h _{> y}l_{. The workers}

search for jobs and firms search for workers and the labour force is normalised at one. For simplicity, we assume that firms may supply vacancies directed towards natives or immigrants. We then include the two features, which may differ for immigrants and natives, influencing their labour market performance differently and thereby their educational decision - namely negative attitudes towards immigrants and networking effects.4

Immigrants may be harmed by negative attitudes towards them at their workplace, resulting in separation from the job. The reason may be many-fold: negative attitudes against immigrants may imply that a firm needs to deal with unexpected issues in the firm or with clients, and/or the immigrant voluntary quits. Hence, immigrants face a random negative shock. We therefore assume that the separation rate, sm

i , m= h, l, i = N, I, may be increasing in negative attitudes

towards immigrants, am_{, m = h, l, giving a separation rate for immigrants of s}m

I = sN(1 + am)

and a separation rate for natives of sh

N = slN = sN. Negative attitudes may (among other things)

themselves be influenced by the fraction of immigrants in an area, an issue we will return to below. On the other hand, more immigrants may make it easier to obtain employment through net- working. We here follow Fontaine (2007) by assuming that networking, λm

i , i = N, I, m = h, l

is increasing in the number of people of the same origin as the individual. We assume that net- working for high productivity immigrants and natives is given by: λh

I = th I(1− ˆeI) (N+I)(1− ˆeI) = t h_{I and} λh N = th_{(N+I)(1− ˆ}N(1− ˆeNe)N) = t

h_{N = t}h_{(1 − I) as N + I = 1, and that networking for low productivity}

immigrants and natives is given by: λl

I = tl(N+I) ˆIeˆIeI = t

l_I and λl

N = tl(N+I) ˆNeˆNeN = t

l_N = tl(1 − I)

as N + I = 1, where 0 < tm _<1, m = h, l, and ˆe

i, i= N, I is the number of low-educated workers

and 1 − ˆei, i= N, I, is the number of educated workers. One may argue that a very large number

of own ethnicity may not be as important as a relative smaller number, a potential network may grow so big that it is not really a usually network in terms of employment perspectives. This could be included in the analysis by changing the functional form of the network variable, so that it is increasing in the number the worker’s own nationality but at a decreasing rate. We will return to

4_{In Larsen and Waisman 2012, it is assumed that it is not possible for firms to direct their search to either} immigrants or natives. Therefore, any negative impact on immigrants, will through changed vacancy supply also affect natives. As the present paper also include educational choice and networking we, for simplicity, keep this additional channel out of the present set-up.

this issue below. 2.1 Matching

We assume that firms advertise Vm

i , i= N, I, m = h, l vacancies. Unemployment rates are given

by um

i , i = N, I, m = h, l and there are Lmi , i = N, I, m = h, l employees. Labour market

tightness by the ethnic group is given by θm

i = (Vim + λmi Lmi )/umi , where the transition rate for

an unemployed worker is given by f(θm

i ) and for the firm it is q(θim). We assume that the worker

transition rate is increasing in labour market tightness and at a decreasing rate, ∂ (f(θm

i )) /∂θim>

0, ∂2_(f(θm

i )) / (∂θim)2 <0 and the firm’s transition rate is decreasing in labour market tightness

at a decreasing rate, ∂ (q(θm

i )) /∂θmi <0 and ∂2(q(θim)) / (∂θmi )2>0.

2.2 The Firm

The firm chooses the number of vacancies so as to maximise profits subject to negative attitudes towards immigrants and subject to networking effects. We assume, for simplicity, that firms can direct their search towards natives or immigrants and that each worker produces ym_{, m= h, l and}

receives the bargained wage, wm

i , i= N, I, m = h, l. We denote the discount rate by ρ and hiring

costs are increasing in productivity, kym_{, m} = h, l. A firm chooses the number of vacancies to

advertise, Vm

i , i= N, I, m = h, l and takes into account that its employees also produce applicants

through networking. Each firm hiring natives or immigrants solves the following Bellman equation:

ρΠi(Lmi ) = max (ymLmi − wim− kymVim+ Πi(Lmi )) , i = N, I, m = h, l, s.t. (1)

˙Lm

N = (λmNLm+ VNm)q(θmN) − sNLmN, m= h, l, (2)

˙Lm

I = (λmI Lm+ VIm)q(θmI ) − smI LmI , m= h, l. (3)

Firms choose their optimal number of employees, using two methods of search: advertising by the firm or networking, which happens at the rate λm

i Lmj f(θim), i = N, I. Separation rates

for immigrants, sm

I = sN(1 + am) ≥ sN, which depend on negative attitudes, am, m = h, l may

and the firm may be dissolved more often than matches for natives and also may differ for high- and low-educated workers. This implies that, for given networking, the expected profitability of a firm employing natives may be different than the expected profitability of employing a high- and/or low-educated immigrant.

With identical firms, using equations (1)-(3) and Kuhn-Tucker conditions, we obtain the non- trivial solution in the steady state determining labour market tightness, θm

i , i= N, I,m = h, l: kym q(θ_Nm) = ym_{− w}m N ρ+ sN− λm_Nq(θ_Nm) , ky m q(θm_I ) = ym_{− w}m I ρ+ sN(1 + am) − λm_I q(θ_Im) . (4)

The partial equilibrium results are the following: more severe negative attitudes, a higher am_,

will tend to reduce labour market tightness and more networking, a higher λm

i , will raise labour

market tightness for the firm hiring the specific type, for either immigrants or natives. 2.3 The Worker

Let Um

i be the value of being an unemployed worker and Emi , m = h, l, i = N, I be the value of

being an employed worker. The values are determined by

ρU_im = f(θm_i )(E_im− Uim) − Γ (m) c (ei) , i = N, I, m = h, l, (5)

ρE_Im = wm_I + sm_I (U_Im_{− EI}m) − Γ (m) c (ei) , m = h, l (6)

ρE_Nm= w_Nm+ sN(UNm− ENm) − Γ (m) c (ei) , m = h, l. (7)